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        <title>Measuring Gender Bias in Natural Language Processing: Incorporating Gender-Neutral Linguistic Forms for Non-Binary Gender Identities in Abusive Speech Detection</title>
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    <name type="personal">
        <namePart type="given">Nasim</namePart>
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        <namePart type="given">Kinshuk</namePart>
        <namePart type="family">Sengupta</namePart>
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    <name type="personal">
        <namePart type="given">Sarah</namePart>
        <namePart type="given">Jane</namePart>
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            <title>Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing</title>
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            <namePart type="given">Ruslan</namePart>
            <namePart type="family">Mitkov</namePart>
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        <name type="personal">
            <namePart type="given">Galia</namePart>
            <namePart type="family">Angelova</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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            <publisher>INCOMA Ltd., Shoumen, Bulgaria</publisher>
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    <abstract>Predictions from machine learning models can reflect bias in the data on which they are trained. Gender bias has been shown to be prevalent in natural language processing models. The research into identifying and mitigating gender bias in these models predominantly considers gender as binary, male and female, neglecting the fluidity and continuity of gender as a variable. In this paper, we present an approach to evaluate gender bias in a prediction task, which recognises the non-binary nature of gender. We gender-neutralise a random subset of existing real-world hate speech data. We extend the existing template approach for measuring gender bias to include test examples that are gender-neutral. Measuring the bias across a selection of hate speech datasets we show that the bias for the gender-neutral data is closer to that seen for test instances that identify as male than those that identify as female.</abstract>
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        <url>https://aclanthology.org/2023.ranlp-1.119</url>
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        <date>2023-09</date>
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            <start>1121</start>
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%0 Conference Proceedings
%T Measuring Gender Bias in Natural Language Processing: Incorporating Gender-Neutral Linguistic Forms for Non-Binary Gender Identities in Abusive Speech Detection
%A Sobhani, Nasim
%A Sengupta, Kinshuk
%A Delany, Sarah Jane
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing
%D 2023
%8 September
%I INCOMA Ltd., Shoumen, Bulgaria
%C Varna, Bulgaria
%F sobhani-etal-2023-measuring
%X Predictions from machine learning models can reflect bias in the data on which they are trained. Gender bias has been shown to be prevalent in natural language processing models. The research into identifying and mitigating gender bias in these models predominantly considers gender as binary, male and female, neglecting the fluidity and continuity of gender as a variable. In this paper, we present an approach to evaluate gender bias in a prediction task, which recognises the non-binary nature of gender. We gender-neutralise a random subset of existing real-world hate speech data. We extend the existing template approach for measuring gender bias to include test examples that are gender-neutral. Measuring the bias across a selection of hate speech datasets we show that the bias for the gender-neutral data is closer to that seen for test instances that identify as male than those that identify as female.
%U https://aclanthology.org/2023.ranlp-1.119
%P 1121-1131

Markdown (Informal)

[Measuring Gender Bias in Natural Language Processing: Incorporating Gender-Neutral Linguistic Forms for Non-Binary Gender Identities in Abusive Speech Detection](https://aclanthology.org/2023.ranlp-1.119) (Sobhani et al., RANLP 2023)

• Measuring Gender Bias in Natural Language Processing: Incorporating Gender-Neutral Linguistic Forms for Non-Binary Gender Identities in Abusive Speech Detection (Sobhani et al., RANLP 2023)

ACL

• Nasim Sobhani, Kinshuk Sengupta, and Sarah Jane Delany. 2023. Measuring Gender Bias in Natural Language Processing: Incorporating Gender-Neutral Linguistic Forms for Non-Binary Gender Identities in Abusive Speech Detection. In Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing, pages 1121–1131, Varna, Bulgaria. INCOMA Ltd., Shoumen, Bulgaria.